Dynamic Effects of Inter-Organizational Dependence in Innovation Networks on Corporate Innovation Performance

Abstract Existing scholarship has largely prioritized the static structural features of innovation networks, while the dynamic evolution of inter-organizational dependence in such networks has been largely neglected. Addressing this gap, this study employs negative binomial regression analysis on the panel data (spanning 2010 to 2018) of collaborative patents from Chinese listed firms. The aim was to investigate how inter-organizational dependence shapes corporate innovation performance contingent on an increase in cooperation duration and the number of partners (i.e., innovation network scale). The results indicate that longer cooperation duration strengthens relational dependence and enhances innovation performance within stable network boundaries. An expanded network scale weakens inter-organizational knowledge dependence and reinforces structural dependence, collectively facilitating innovation improvements. This study empirically verifies the dynamic mechanism of time-scale dependence shaping the performance of innovation networks. The findings provide insightful theoretical implications and practical guidelines for cultivating resilient corporate innovation ecosystems. Departing from the static paradigm prevailing in innovation network research, this study unveils the nonlinear evolutionary patterns of multi-dimensional inter-organizational dependence along the temporal and network-scale dimensions. It offers an advanced theoretical lens for interpreting the life-cycle dynamics of corporate innovation networks.